This article introduce the genetic algorithm to solve the routing problem with QoS requirements, solve the traditional routing algorithms in completely the limitation of the NP problem. QoS routing optimization model is given, and the detailed design of genetic algorithm encoding scheme, fitness function, selection operator, crossover operator and mutation operator. Finally, the simulation expe...

Genetic algorithms are optimisation algorithms and mimic the natural process of evolution. Important operators used in genetic algorithms are selection, crossover and mutation. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Crossover and mutation operators are used to introduce diversity in the popul...

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator andmutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more ab...

we introduce a new concept of general $g$-$eta$-monotone operator generalizing the general $(h,eta)$-monotone operator cite{arvar2, arvar1}, general $h-$ monotone operator cite{xiahuang} in banach spaces, and also generalizing $g$-$eta$-monotone operator cite{zhang}, $(a, eta)$-monotone operator cite{verma2}, $a$-monotone operator cite{verma0}, $(h, eta)$-monotone operator cite{fanghuang}, $h$-...

This paper is concerned with the determination of optimal sensor locations for structural modal identification in a strap-braced cold formed steel frame based on an improved genetic algorithm (IGA). Six different optimal sensor placement performance indices have been taken as the fitness functions two based on modal assurance criterion (MAC), two based on maximization of the determinant of a Fi...

The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: ...

This paper presents a comparison in the performance analysis between a newly developed crossover operator called Rayleigh Crossover (RX) and an existing crossover operator called Laplace Crossover (LX). Coherent to the previously defined Scaled Truncated Pareto Mutation (STPM) operator to form two (2) generational RCGAs called RX-STPM and LX-STPM, both crossovers are utilized. A set of ten (10)...

This work analyzes fitness landscapes for the image filter design problem approached using functional-level Cartesian Genetic Programming. Smoothness and ruggedness of fitness landscapes are investigated for five genetic operators. It is shown that the mutation operator and the single-point crossover operator generate the smoothest landscapes and thus they are useful for practical applications ...

The purpose of this paper is to discuss the implementation and performance of two genetic operators specifically tuned to solve the Travelling Salesman Problem. The two operators discussed are the Greedy Knot-Cracker crossover operator and a modified version of the 3-opt mutation operator. Specifically the paper shall discuss the motivation for choosing these operators, their implementation, th...

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strate...